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Testing of Multifractional Brownian Motion entropy Article Testing of Multifractional Brownian Motion Michał Balcerek *,† and Krzysztof Burnecki † Faculty of Pure and Applied Mathematics, Hugo Steinhaus Center, Wroclaw University of Science and Technology, Wyspianskiego 27, 50-370 Wroclaw, Poland; [email protected] * Correspondence: [email protected] † These authors contributed equally to this work. Received: 18 November 2020; Accepted: 10 December 2020; Published: 12 December 2020 Abstract: Fractional Brownian motion (FBM) is a generalization of the classical Brownian motion. Most of its statistical properties are characterized by the self-similarity (Hurst) index 0 < H < 1. In nature one often observes changes in the dynamics of a system over time. For example, this is true in single-particle tracking experiments where a transient behavior is revealed. The stationarity of increments of FBM restricts substantially its applicability to model such phenomena. Several generalizations of FBM have been proposed in the literature. One of these is called multifractional Brownian motion (MFBM) where the Hurst index becomes a function of time. In this paper, we introduce a rigorous statistical test on MFBM based on its covariance function. We consider three examples of the functions of the Hurst parameter: linear, logistic, and periodic. We study the power of the test for alternatives being MFBMs with different linear, logistic, and periodic Hurst exponent functions by utilizing Monte Carlo simulations. We also analyze mean-squared displacement (MSD) for the three cases of MFBM by comparing the ensemble average MSD and ensemble average time average MSD, which is related to the notion of ergodicity breaking. We believe that the presented results will be helpful in the analysis of various anomalous diffusion phenomena. Keywords: multifractional Brownian motion; autocovariance function; power of the statistical test; Monte Carlo simulations 1. Introduction Over the last decades, massive advances in single-particle tracking (SPT), partially based on superresolution microscopy of fluorescently tagged tracers, or fluorescence correlation spectroscopy allow experimentalists to obtain insight into the motion of submicron tracer particles or even single molecules in complex environments, such as living biological cells, down to nanometer precision and at submillisecond time resolution [1,2]. The observed data obtained by SPT experiments often show pronounced deviations from Brownian motion, namely, anomalous diffusion of the power-law form 2 a EX(t) ' Kat (1) of the mean-squared displacement (MSD) is observed [3–8]. Ka is the anomalous diffusion coefficient. Depending on the magnitude of the anomalous diffusion exponent a we distinguish subdiffusion for 0 < a < 1 from superdiffusion with a > 1 [5,8]. Subdiffusion is typically observed for submicron particles in both bacterial and eukaryotic cells [6,9–13], in artificially crowded [14,15] and structured [16–19] liquids, in pure and protein-crowded lipid bilayer systems [12,20–25], as well as in groundwater systems [26]. Superdiffusion occurs in the presence of active motion, for instance, in living biological cells [27–29] or due to bulk-surface exchange [30,31]. While typically anomalous Entropy 2020, 22, 1403; doi:10.3390/e22121403 www.mdpi.com/journal/entropy Entropy 2020, 22, 1403 2 of 17 diffusion refers to the power-law behavior (1) with a fixed a, an increasing number of systems are reported in which the local scaling exponent of the MSD (1) is an explicit function of time, a(t). Such transient behavior has, for instance, been observed for green fluorescent proteins in cells or for the motion of lipid molecules in protein-crowded bilayer membranes [25,32]. Fractional Brownian motion (FBM) introduced by Kolmogorov in 1940 and rediscovered by Mandelbrot and van Ness in 1968 [33–35] is a generalization of the classical Brownian motion (BM). Most of its statistical properties are characterized by the self-similarity (Hurst) index 0 < H < 1. FBM is D H H-self-similar, namely for every c > 0 we have BH(ct) = c BH(t) in the sense of all finite dimensional distributions, and has stationary increments. It is the only Gaussian process satisfying these properties. FBM is the overdamped description for viscoelastic motion and thus intimately connected to the fractional Langevin processes, an attractive framework for many physical systems [36], for instance, 2 2 2H of lipid molecules in bilayer membranes [22,25,37]. The second moment of FBM reads EBH(t) = s t , 2 2 where EBH(1) = s > 0. As a consequence, for H < 1/2 we obtain subdiffusive dynamics with persistent motion, whereas for H > 1/2, the process is superdiffusive and antipersistent. Since FBM is the classical model for power-law dependence a number of statistical tests have been already introduced for this process in the literature. Let us mention here the tests based on the autocovariance function (ACVF), MSD, and detrending moving average statistics [38–40]. FBM has stationary increments that do not allow us to model processes whose regularity of paths and “memory depth” change in time [41]. Several generalizations of FBM have been proposed recently. One of these, called multifractional Brownian motion (MFBM), was proposed by Peltier and Véhel [42] with time-varying Hurst exponent H(t) which is a Hölder function. The variance at time 2 2H(t) t of the MFBM BH(t)(t) is given by Var(BH(t)(t)) = s t [43]. The time-varying Hurst exponent H(t) characterizes the path regularity of the process at time t: sample paths near t with small Ht, close to 0, are space-filling and highly irregular, while paths with large Ht, close to 1, are very smooth. The variance constant s2 determines the “energy level” of the process. This natural extension of FBM results in some loss of some of FBMs basic properties, in particular, the increments of MFBM are non-stationary and the process is no longer self-similar. Other, similar generalizations are limited to a piecewise constant H [44] but, what is important from a data analysis point of view, is that they lead to continuous Gaussian processes with stationary increments. Let us also mention an idea involving an appropriate class of covariance functions. Ryvkina [45] uses such covariance functions to define Gaussian processes to extend FBM and MFBM to a class of fractional Brownian motions with a variable Hurst parameter parameterized by a set of all measurable functions with values in (1/2, 1), and different from MFBMs. However, from a biological data point of view, such a range for H values is not practical since it only corresponds to a superdiffusive (long-range dependent) case. MFBMs have become popular as flexible models in describing real-life signals of high-frequency features in geoscience, microeconomics, and turbulence, to name a few [43]. They are closely related to the notion of transient diffusion dynamics observed in biological experiments. The article is structured as follows. In Section2, the MFBM is defined and its basic properties are presented. We also recall formulas for the ensemble average MSD, time average MSD, and present three Hurst exponent functions that will be analyzed in the sequel. In Section3, the main results are presented. We introduce a statistical test on MFBM based on its ACVF which is presented as a quadratic form. Next, the power of the test is studied for the three cases corresponding to different Hurst exponent functions. We show the areas where the test is very strong in distinguishing between the processes and the cases when it fails in this respect. Finally, Section4 summarizes and concludes our work. 2. Model and Methods Let us start with a definition of the MFBM. Entropy 2020, 22, 1403 3 of 17 Definition 1. (Multifractional Brownian motion). Process BH(t)(t) is called a multifractional Brownian t≥0 motion (MFBM) if it is a centered Gaussian process with covariance function H(t)+H(s) H(t)+H(s) H(t)+H(s) Cov(BH(t)(t), BH(s)(s)) = D(H(t), H(s)) t + s − jt − sj , (2) where p s2 G(2x + 1)G(2y + 1) sin(px) sin(py) D(x, y) = (3) x+y 2G(x + y + 1) sin p 2 for some s > 0 and Hölder function H : [0, ¥) ! [a, b] ⊂ (0, 1) of some exponent b > 0 [46]. 2 ( ) = 2 2H(t) ( ) The second moment of MFBM scales as E BH(t) t s t . Hence, we will call H t the Hurst exponent function. Furthermore, for H(t) ≡ H 2 (0, 1) MFBM becomes standard FBM. de f In general, MFBM has non-stationary increments. Its increment process Y(t) = BH(t+1)(t + 1) − BH(t)(t) possesses the long-range dependence property, in the sense that ¥ 8d > 0, 8s ≥ 0 ∑ jCorr (Y(s), Y(s + kd)) j = +¥, k=0 ( ( ) ( )) where Corr is the correlation function, i.e., Corr(Y(t), Y(s)) = pCov Y t ,Y s [46]. E2Y(t)E2Y(s) Furthermore, the function H(t) can be pointwise interpreted as a local self-similarity parameter, i.e., ! BH(u+#t)(u + #t) − BH(u)(u) lim = s(u) (BH(t))t2 , #!0+ #H(u) R+ t2R+ where BH is a fractional Brownian motion with index H ≡ H(u), s(u) is a scaling function [47] and the convergence is on the space of continuous functions endowed with the topology of the uniform convergence on compact sets. 2.1. Mean-Squared Displacement Let us now recall different estimators of the MSD for a sample of n trajectories X1, X2, ... Xn, each with N observations, that is, Xi consists of Xi(t1), Xi(t2), ... , Xi(tN) equally spaced in time, i = 1, 2, . , n. Ensemble average MSD (EAMSD) is defined as follows: n 1 2 EAMSD(t = mDt) = ∑ (Xk(t1 + t) − Xk(t1)) , (4) n k=1 where m = 1, .
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